# Universal LLM Function Specification
```
Function: process_and_respond
Input: text string (user query/request)
Output: text string (formatted response)
Purpose: Transform input text into structured, prioritized response
while handling constraints and maintaining reliability
Pseudo-implementation:
function process_and_respond(input_text):
# 1. Process Input
# Extract core query/intent from input text
# No assumptions about processing method
# 2. Structure Response
response = concat(
# Priority Block 1: Essential (Must complete)
format(
"Input understood: " + summarize(input_text)
+ "\n\n"
+ get_critical_information()
+ "\nEND_ESSENTIAL\n\n"
),
# Priority Block 2: Important (Should complete)
format(
get_important_details()
+ "\nEND_IMPORTANT\n\n"
),
# Priority Block 3: Additional (May complete)
format(
get_additional_context()
+ "\nEND_ADDITIONAL"
)
)
return response
Where:
- format(): Applies consistent structure
- concat(): Joins text blocks
- summarize(): Extracts core meaning
- get_critical_information(): Highest priority content
- get_important_details(): Supporting content
- get_additional_context(): Optional content
Structure:
Each block contains complete thoughts that provide value
even if subsequent blocks are truncated
Priority:
1. Core understanding and essential response
2. Supporting information and context
3. Additional details and next steps
Completion Markers:
- END_ESSENTIAL
- END_IMPORTANT
- END_ADDITIONAL
Implementation Notes:
1. Assume minimal feature set
2. No specific language features required
3. No external dependencies assumed
4. Handles truncation gracefully
5. Works with statistical text completion
```
Key Properties:
1. Input Handling:
- Accepts any text input
- No format assumptions
- No preprocessing requirements
2. Output Generation:
- Prioritized structure
- Clear section markers
- Complete thoughts
- Natural completion points
3. Error Handling:
- Graceful degradation
- Value preservation
- Clear boundaries
- Recovery points
4. Content Priority:
```
Priority 1 (Must):
- Understanding confirmation
- Core response
- Essential information
Priority 2 (Should):
- Supporting details
- Key examples
- Context
Priority 3 (May):
- Additional information
- Next steps
- Further exploration
```
5. Implementation Independence:
- No language assumptions
- No library dependencies
- No special features required
- Minimal implementation needs
Usage Example:
```
Input: "Explain quantum computing"
Output:
Input understood: Request for quantum computing explanation
[Essential Information]
Quantum computing uses quantum mechanics for computation.
Key concepts:
- Qubits instead of classical bits
- Superposition and entanglement
- Parallel processing capability
END_ESSENTIAL
[Important Details]
Current state:
- Experimental technology
- Limited practical applications
- Major companies developing systems
END_IMPORTANT
[Additional Context]
Applications include:
- Cryptography
- Drug discovery
- Optimization problems
END_ADDITIONAL
```
This function definition:
1. Makes minimal assumptions
2. Maintains structure
3. Preserves value
4. Handles constraints
5. Enables consistency
Would you like me to:
1. Elaborate on any aspect?
2. Show more usage examples?
3. Explore specific implementations?</antArtifact>